Field
This application relates to mobile and wearable devices, specifically to methodologies to obtain contextual information.
Discussion of Related Art:
Satellite-based approaches to estimate location and velocity are known to be very power-hungry, draining the battery of mobile devices in a short time. This is one of the disadvantages of having to work with signals transmitted from satellites. Moreover, the accuracy of satellite-based systems for mobile devices can be low, especially in urban areas with canyon effects, which represent additional obstacles for the weak satellite signals. Furthermore, these satellite-based systems are not reliable in indoor environments. Consequently, there is a need for an accurate methodology that can deliver contextual information including velocity for pedestrians in any condition, even when they are indoors.
Computer vision based systems make use of several cameras in a lab environment and markers attached at critical points of the individual's body. Cameras record the body movement, and the video processing can deliver accurate measurements. However, their sophisticated setup and requirements make them laborious, expensive, space-constrained and difficult to implement in real-time applications.
On the other hand, and thanks to the advancements in Micro Electro Mechanical Systems (MEMS) and wireless communications, monitoring systems based on wearable sensors are making progress. In this sense, the analysis of spatio-temporal parameters of human gait is a complex and difficult task. Moreover, sophisticated hardware requirements can raise the cost of the system.
For many applications, there is a need to efficiently leverage the sensors embedded in mobile and/or wearable devices to provide contextual information in real-time directly to the user.